数字农科院2.0

Identification of Pine Wilt Disease Infected Wood Using UAV RGB Imagery and Improved YOLOv5 Models Integrated with Attention Mechanisms

文献类型： 外文期刊

作者： Peng Zhang;Zhichao Wang;Yuan Rao;Jun Zheng;Ning Zhang;Degao Wang;Jianqiao Zhu;Yifan Fang;Xiang Gao

作者机构：

关键词： attention mechanism;diseased wood;pine wilt disease;UAV remote sensing;YOLOv5

期刊名称： Forests

ISSN： 1999-4907

年卷期： 2023 年 14 卷 3 期

页码：

收录情况： SCIE（2023版） ; ; EI（2023版）

摘要： Pine wilt disease (PWD) is a great danger, due to two aspects: no effective cure and fast dissemination. One key to the prevention and treatment of pine wilt disease is the early detection of infected wood. Subsequently, appropriate treatment can be applied to limit the further spread of pine wilt disease. In this work, a UAV (Unmanned Aerial Vehicle) with a RGB (Red, Green, Blue) camera was employed as it provided high-quality images of pine trees in a timely manner. Seven flights were performed above seven sample plots in northwestern Beijing, China. Then, raw images captured by the UAV were further pre-processed, classified, annotated, and formed the research datasets. In the formal analysis, improved YOLOv5 frameworks that integrated four attention mechanism modules, i.e., SE (Squeeze-and-Excitation), CA (Coordinate Attention), ECA (Efficient Channel Attention), and CBAM (Convolutional Block Attention Module), were developed. Each of them had been shown to improve the overall identification rate of infected trees at different ranges. The CA module was found to have the best performance, with an accuracy of 92.6%, a 3.3% improvement over the original YOLOv5s model. Meanwhile, the recognition speed was improved by 20 frames/second compared to the original YOLOv5s model. The comprehensive performance could well support the need for rapid detection of pine wilt disease. The overall framework proposed by this work shows a fast response to the spread of PWD. In addition, it requires a small amount of financial resources, which determines the duplication of this method for forestry operators.

分类号：

  • 相关文献

[1]Sunflower-YOLO: Detection of sunflower capitula in UAV remote sensing images. Rui Jing,Qinglin Niu,Yuyu Tian,Heng Zhang,Qingqing Zhao,Zongpeng Li,Xinguo Zhou,Dongwei Li. 2024

[2]An Unstructured Orchard Grape Detection Method Utilizing YOLOv5s. Wang, Wenhao,Shi, Yun,Liu, Wanfu,Che, Zijin. 2024

[3]Small target tea bud detection based on improved YOLOv5 in complex background. Mengjie Wang,Yang Li,Hewei Meng,Zhiwei Chen,Zhiyong Gui,Yaping Li,Chunwang Dong. 2024

[4]Lightweight Structure and Attention Fusion for In-Field Crop Pest and Disease Detection. Zijing Luo,Yunsen Liang,Naimin Kong,Lirui Liang,Wenjun Peng,Yujie Yao,Chi Qin,Xiaohan Lu,Mingman Xu,Yining Zhang,Chenyang Lin,Chengyao Jiang,Mengyao Li,Yangxia Zheng,Yameng Jiang,Wei Lu. 2025

[5]基于区域亮度自适应校正的茶叶嫩芽检测模型. 吕军,方梦瑞,姚青,武传宇,贺盈磊,边磊,钟小玉. 2021

[6]基于YOLOv5深度学习的茶叶嫩芽估产方法. 徐海东,马伟,谭彧,刘星星,郑永军,田志伟. 2022

[7]Applicability of UAV-based optical imagery and classification algorithms for detecting pine wilt disease at different infection stages. Zhang N.,Chai X.,Li N.,Zhang J.,Sun T.. 2023

[8]The construction and evaluation of secretory expression engineering bacteria for the trans-Cry3Aa-T-HasA fusion protein against the Monochamus alternatus vector. Han, Xiaohong,Huang, Chenyan,Qi, Huan,Zhu, Yukun,Hu, Xinran,Wen, Yingxin,Long, Yirong,Xu, Lei,Zhang, Feiping. 2024

[9]Flood Disaster Monitoring and Emergency Assessment Based on Multi-Source Remote Sensing Observations. Lei, Tianjie,Wang, Jiabao,Li, Xiangyu,Wang, Weiwei,Shao, Changliang,Liu, Baoyin. 2022

[10]Precise Estimation of Sugarcane Yield at Field Scale with Allometric Variables Retrieved from UAV Phantom 4 RTK Images. Qiuyan Huang,Juanjuan Feng,Maofang Gao,Shuangshuang Lai,Guangping Han,Zhihao Qin,Jinlong Fan,Yuling Huang. 2024

[11]Enhanced cotton chlorophyll content estimation with UAV multispectral and LiDAR constrained SCOPE model. Puchen Yan,Yangming Feng,Qisheng Han,Zongguang Hu,Xi Huang,Kaikai Su,Shaozhong Kang. 2024

[12]A self-adaptive parallel image stitching algorithm for unmanned aerial vehicles in edge computing environments. Xin Xu,Li Zhang,Jibo Yue,Heming Zhong,Ying Wang,Jie Liu,Yanhui Lu,Hongbo Qiao. 2024

[13]Cross-scale estimating of forage nitrogen in alpine grassland integrating UAV imagery and Sentinel-2 data. Jinlong Gao,Mengwei Han,Dongmei Zhang,Zhanping Ma,Yongkang Zhang,Shuai Fu,Tiangang Liang. 2025

[14]Diagnosis of nitrogen nutrition in winter wheat across years based on multi-source remote sensing data from unmanned aerial vehicles. Deshan Chen,Yitian Chen,Hui Zhang,Jinrui Liu,Qian Cheng,Fuyi Duan,Xiaohui Kuang,Wanna Fu,Jie Liu,Zhen Chen. 2025

[15]Research on machine vision and deep learning based recognition of cotton seedling aphid infestation level. Xin Xu,Jing Shi,Yongqin Chen,Qiang He,Liangliang Liu,Tong Sun,Ruifeng Ding,Yanhui Lu,Chaoqun Xue,Hongbo Qiao. 2023

[16]A Detection Model for Cucumber Root-Knot Nematodes Based on Modified YOLOv5-CMS. Wang C.,Sun S.,Zhao C.,Mao Z.,Wu H.,Teng G.. 2022

[17]Detection and Counting of Maize Leaves Based on Two-Stage Deep Learning with UAV-Based RGB Image. Xu, Xingmei,Wang, Lu,Shu, Meiyan,Liang, Xuewen,Ghafoor, Abu Zar,Liu, Yunling,Ma, Yuntao,Zhu, Jinyu. 2022

[18]Deep-Learning-Based Rice Phenological Stage Recognition. Jiale Qin,Tianci Hu,Jianghao Yuan,Qingzhi Liu,Wensheng Wang,Jie Liu,Leifeng Guo,Guozhu Song. 2023

[19]Tea Bud Detection and 3D Pose Estimation in the Field with a Depth Camera Based on Improved YOLOv5 and the Optimal Pose-Vertices Search Method. Chen, Zhiwei,Chen, Jianneng,Li, Yang,Gui, Zhiyong,Yu, Taojie. 2023

[20]Lightweight SM-YOLOv5 Tomato Fruit Detection Algorithm for Plant Factory. Xinfa Wang,Zhenwei Wu,Meng Jia,Tao Xu,Canlin Pan,Xuebin Qi,Mingfu Zhao. 2023

作者其他论文 更多>>